Overview

Dataset statistics

Number of variables19
Number of observations740774
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory107.4 MiB
Average record size in memory152.0 B

Variable types

DateTime2
Numeric16
Categorical1

Alerts

C_SIZE has a high cardinality: 193090 distinct valuesHigh cardinality
UNDERLYING_LAST is highly overall correlated with STRIKEHigh correlation
DTE is highly overall correlated with C_VEGA and 1 other fieldsHigh correlation
C_DELTA is highly overall correlated with C_LAST and 3 other fieldsHigh correlation
C_GAMMA is highly overall correlated with C_IV and 5 other fieldsHigh correlation
C_VEGA is highly overall correlated with DTE and 1 other fieldsHigh correlation
C_THETA is highly overall correlated with STRIKE_DISTANCE_PCTHigh correlation
C_RHO is highly overall correlated with DTE and 4 other fieldsHigh correlation
C_IV is highly overall correlated with C_GAMMA and 3 other fieldsHigh correlation
C_LAST is highly overall correlated with C_DELTA and 5 other fieldsHigh correlation
C_BID is highly overall correlated with C_DELTA and 5 other fieldsHigh correlation
C_ASK is highly overall correlated with C_DELTA and 5 other fieldsHigh correlation
STRIKE is highly overall correlated with UNDERLYING_LAST and 1 other fieldsHigh correlation
STRIKE_DISTANCE is highly overall correlated with C_GAMMA and 1 other fieldsHigh correlation
STRIKE_DISTANCE_PCT is highly overall correlated with C_GAMMA and 2 other fieldsHigh correlation
C_GAMMA is highly skewed (γ1 = 72.98957532)Skewed
C_VEGA is highly skewed (γ1 = -276.0944803)Skewed
C_IV is highly skewed (γ1 = 25.82166229)Skewed
C_VOLUME is highly skewed (γ1 = 35.21903634)Skewed
C_GAMMA has 8633 (1.2%) zerosZeros
C_BID has 14468 (2.0%) zerosZeros

Reproduction

Analysis started2023-10-07 23:50:12.021608
Analysis finished2023-10-07 23:52:28.161938
Duration2 minutes and 16.14 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Distinct1884
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
Minimum2016-01-04 00:00:00
Maximum2023-06-30 00:00:00
2023-10-07T23:52:28.407050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:28.974531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

UNDERLYING_LAST
Real number (ℝ)

Distinct1767
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.88008
Minimum90.34
Maximum506.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-10-07T23:52:29.463476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum90.34
5-th percentile109.1
Q1133.49
median156.49
Q3203.78
95-th percentile353.63
Maximum506.19
Range415.85
Interquartile range (IQR)70.29

Descriptive statistics

Standard deviation78.132088
Coefficient of variation (CV)0.42490783
Kurtosis2.8848006
Mean183.88008
Median Absolute Deviation (MAD)28.9
Skewness1.7307023
Sum1.3621358 × 108
Variance6104.6231
MonotonicityNot monotonic
2023-10-07T23:52:29.983478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1715
 
0.2%
142.57 1642
 
0.2%
154.53 1565
 
0.2%
116.55 1530
 
0.2%
142.53 1467
 
0.2%
131.74 1454
 
0.2%
115.05 1381
 
0.2%
121.39 1305
 
0.2%
125.9 1279
 
0.2%
145.38 1208
 
0.2%
Other values (1757) 726228
98.0%
ValueCountFrequency (%)
90.34 251
< 0.1%
90.52 254
< 0.1%
92.04 192
< 0.1%
92.51 219
< 0.1%
92.72 288
< 0.1%
92.82 262
< 0.1%
93.24 289
< 0.1%
93.34 191
< 0.1%
93.42 252
< 0.1%
93.43 219
< 0.1%
ValueCountFrequency (%)
506.19 770
0.1%
503.68 611
0.1%
500.04 952
0.1%
499.3 865
0.1%
499.23 800
0.1%
497.48 934
0.1%
473.1 913
0.1%
462.82 918
0.1%
462.08 849
0.1%
460.03 937
0.1%
Distinct412
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
Minimum2016-01-08 00:00:00
Maximum2025-12-19 00:00:00
2023-10-07T23:52:30.495602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:31.041212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

EXPIRE_UNIX
Real number (ℝ)

Distinct412
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6113192 × 109
Minimum1.4522868 × 109
Maximum1.766178 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-10-07T23:52:31.683572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.4522868 × 109
5-th percentile1.4873652 × 109
Q11.5750612 × 109
median1.6179984 × 109
Q31.6579152 × 109
95-th percentile1.702674 × 109
Maximum1.766178 × 109
Range3.138912 × 108
Interquartile range (IQR)82854000

Descriptive statistics

Standard deviation63190040
Coefficient of variation (CV)0.039216338
Kurtosis-0.28615525
Mean1.6113192 × 109
Median Absolute Deviation (MAD)41126400
Skewness-0.44718903
Sum1.1936234 × 1015
Variance3.9929811 × 1015
MonotonicityNot monotonic
2023-10-07T23:52:32.161837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1642798800 31898
 
4.3%
1655496000 29082
 
3.9%
1610744400 25497
 
3.4%
1624046400 25287
 
3.4%
1663358400 24121
 
3.3%
1631908800 20232
 
2.7%
1674248400 18690
 
2.5%
1592596800 18173
 
2.5%
1579294800 17044
 
2.3%
1686945600 14865
 
2.0%
Other values (402) 515885
69.6%
ValueCountFrequency (%)
1452286800 35
 
< 0.1%
1452891600 333
 
< 0.1%
1453496400 153
 
< 0.1%
1454101200 338
 
< 0.1%
1454706000 596
0.1%
1455310800 733
0.1%
1455915600 618
0.1%
1456520400 956
0.1%
1457125200 636
0.1%
1457730000 537
0.1%
ValueCountFrequency (%)
1766178000 2068
 
0.3%
1750449600 1814
 
0.2%
1737147600 5422
 
0.7%
1734728400 716
 
0.1%
1726862400 1096
 
0.1%
1719000000 8355
1.1%
1710532800 2801
 
0.4%
1705698000 14713
2.0%
1702674000 2199
 
0.3%
1700254800 819
 
0.1%

DTE
Real number (ℝ)

Distinct1812
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222.63459
Minimum0
Maximum1059
Zeros5816
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-10-07T23:52:32.683530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q131
median128
Q3359
95-th percentile728
Maximum1059
Range1059
Interquartile range (IQR)328

Descriptive statistics

Standard deviation237.00224
Coefficient of variation (CV)1.0645346
Kurtosis0.079793312
Mean222.63459
Median Absolute Deviation (MAD)107
Skewness1.1026104
Sum1.6492192 × 108
Variance56170.061
MonotonicityNot monotonic
2023-10-07T23:52:33.184193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 8387
 
1.1%
16 8259
 
1.1%
15 8233
 
1.1%
23 8086
 
1.1%
22 8063
 
1.1%
24 8055
 
1.1%
10 7833
 
1.1%
21 7682
 
1.0%
14 7626
 
1.0%
9 7453
 
1.0%
Other values (1802) 661097
89.2%
ValueCountFrequency (%)
0 5816
0.8%
1 5728
0.8%
2 5934
0.8%
3 6283
0.8%
4 5458
0.7%
6 235
 
< 0.1%
6.96 166
 
< 0.1%
7 6182
0.8%
7.04 92
 
< 0.1%
7.96 179
 
< 0.1%
ValueCountFrequency (%)
1059 3
 
< 0.1%
1058 11
< 0.1%
1057 15
< 0.1%
1054 14
< 0.1%
1053 13
< 0.1%
1052 11
< 0.1%
1051 20
< 0.1%
1050 18
< 0.1%
1047 16
< 0.1%
1046 12
< 0.1%

C_DELTA
Real number (ℝ)

Distinct99334
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59617125
Minimum0
Maximum1
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-10-07T23:52:33.566344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01853
Q10.30461
median0.689475
Q30.88756
95-th percentile0.98674
Maximum1
Range1
Interquartile range (IQR)0.58295

Descriptive statistics

Standard deviation0.32890263
Coefficient of variation (CV)0.55169154
Kurtosis-1.1945427
Mean0.59617125
Median Absolute Deviation (MAD)0.233835
Skewness-0.49431646
Sum441628.16
Variance0.10817694
MonotonicityNot monotonic
2023-10-07T23:52:33.862097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5159
 
0.7%
0.00147 79
 
< 0.1%
0.00135 73
 
< 0.1%
0.0012 71
 
< 0.1%
0.00127 69
 
< 0.1%
0.00115 68
 
< 0.1%
0.00102 67
 
< 0.1%
0.00119 66
 
< 0.1%
0.00117 65
 
< 0.1%
0.00116 65
 
< 0.1%
Other values (99324) 734992
99.2%
ValueCountFrequency (%)
0 4
< 0.1%
3 × 10-51
 
< 0.1%
4 × 10-53
< 0.1%
5 × 10-54
< 0.1%
8 × 10-54
< 0.1%
9 × 10-55
< 0.1%
0.0001 2
 
< 0.1%
0.00012 2
 
< 0.1%
0.00013 3
< 0.1%
0.00014 4
< 0.1%
ValueCountFrequency (%)
1 5159
0.7%
0.99999 5
 
< 0.1%
0.99998 11
 
< 0.1%
0.99997 8
 
< 0.1%
0.99996 5
 
< 0.1%
0.99995 11
 
< 0.1%
0.99994 12
 
< 0.1%
0.99993 9
 
< 0.1%
0.99992 10
 
< 0.1%
0.99991 9
 
< 0.1%

C_GAMMA
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct9177
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009146531
Minimum-4 × 10-5
Maximum4.85902
Zeros8633
Zeros (%)1.2%
Negative2572
Negative (%)0.3%
Memory size5.7 MiB
2023-10-07T23:52:34.150822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4 × 10-5
5-th percentile0.00013
Q10.00191
median0.00545
Q30.01098
95-th percentile0.03247
Maximum4.85902
Range4.85906
Interquartile range (IQR)0.00907

Descriptive statistics

Standard deviation0.01506238
Coefficient of variation (CV)1.6467861
Kurtosis17759.658
Mean0.009146531
Median Absolute Deviation (MAD)0.00406
Skewness72.989575
Sum6775.5123
Variance0.00022687529
MonotonicityNot monotonic
2023-10-07T23:52:34.454843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8633
 
1.2%
6 × 10-52410
 
0.3%
5 × 10-52381
 
0.3%
4 × 10-52242
 
0.3%
7 × 10-52210
 
0.3%
9 × 10-52111
 
0.3%
0.0001 2096
 
0.3%
8 × 10-52063
 
0.3%
0.00011 2042
 
0.3%
0.00012 1967
 
0.3%
Other values (9167) 712619
96.2%
ValueCountFrequency (%)
-4 × 10-5199
 
< 0.1%
-3 × 10-5505
 
0.1%
-2 × 10-5823
 
0.1%
-1 × 10-51045
 
0.1%
0 8633
1.2%
1 × 10-51550
 
0.2%
2 × 10-51774
 
0.2%
3 × 10-51934
 
0.3%
4 × 10-52242
 
0.3%
5 × 10-52381
 
0.3%
ValueCountFrequency (%)
4.85902 1
< 0.1%
2.34081 1
< 0.1%
2.03335 1
< 0.1%
1.96583 1
< 0.1%
1.89826 1
< 0.1%
1.88722 1
< 0.1%
1.85446 1
< 0.1%
1.8544 1
< 0.1%
1.55917 1
< 0.1%
1.48943 1
< 0.1%

C_VEGA
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct115456
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.293344
Minimum-831.8998
Maximum20.30734
Zeros4745
Zeros (%)0.6%
Negative221
Negative (%)< 0.1%
Memory size5.7 MiB
2023-10-07T23:52:34.743975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-831.8998
5-th percentile0.00633
Q10.06934
median0.19004
Q30.44005
95-th percentile0.9017635
Maximum20.30734
Range852.20714
Interquartile range (IQR)0.37071

Descriptive statistics

Standard deviation2.2031302
Coefficient of variation (CV)7.5103981
Kurtosis87167.296
Mean0.293344
Median Absolute Deviation (MAD)0.15177
Skewness-276.09448
Sum217301.61
Variance4.8537828
MonotonicityNot monotonic
2023-10-07T23:52:35.043544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4745
 
0.6%
0.00098 119
 
< 0.1%
0.00107 113
 
< 0.1%
0.00079 111
 
< 0.1%
0.00093 111
 
< 0.1%
0.00097 108
 
< 0.1%
0.00083 108
 
< 0.1%
0.00113 107
 
< 0.1%
0.00082 106
 
< 0.1%
0.00078 104
 
< 0.1%
Other values (115446) 735042
99.2%
ValueCountFrequency (%)
-831.8998 1
< 0.1%
-791.84214 1
< 0.1%
-783.30045 1
< 0.1%
-513.19982 1
< 0.1%
-507.50149 1
< 0.1%
-442.80003 1
< 0.1%
-402.8003 1
< 0.1%
-389.9996 1
< 0.1%
-363.49975 1
< 0.1%
-327.80032 1
< 0.1%
ValueCountFrequency (%)
20.30734 1
< 0.1%
19.82675 1
< 0.1%
19.1228 1
< 0.1%
18.58464 1
< 0.1%
18.42335 1
< 0.1%
18.23315 1
< 0.1%
17.98272 1
< 0.1%
17.31834 1
< 0.1%
16.49933 1
< 0.1%
16.31371 1
< 0.1%

C_THETA
Real number (ℝ)

Distinct30815
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.043295572
Minimum-4.99959
Maximum0
Zeros2249
Zeros (%)0.3%
Negative738525
Negative (%)99.7%
Memory size5.7 MiB
2023-10-07T23:52:35.317253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4.99959
5-th percentile-0.13371
Q1-0.05047
median-0.02622
Q3-0.01493
95-th percentile-0.00433
Maximum0
Range4.99959
Interquartile range (IQR)0.03554

Descriptive statistics

Standard deviation0.058013479
Coefficient of variation (CV)-1.3399403
Kurtosis183.11862
Mean-0.043295572
Median Absolute Deviation (MAD)0.01453
Skewness-7.4638835
Sum-32072.234
Variance0.0033655638
MonotonicityNot monotonic
2023-10-07T23:52:35.614317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2249
 
0.3%
-0.01674 224
 
< 0.1%
-0.01611 222
 
< 0.1%
-0.01669 220
 
< 0.1%
-0.01572 220
 
< 0.1%
-0.01872 220
 
< 0.1%
-0.01661 219
 
< 0.1%
-0.01822 218
 
< 0.1%
-0.0171 216
 
< 0.1%
-0.01605 216
 
< 0.1%
Other values (30805) 736550
99.4%
ValueCountFrequency (%)
-4.99959 1
< 0.1%
-2.8047 1
< 0.1%
-2.73061 1
< 0.1%
-2.70521 1
< 0.1%
-2.65492 1
< 0.1%
-2.60507 1
< 0.1%
-2.57964 1
< 0.1%
-2.50014 1
< 0.1%
-1.86002 1
< 0.1%
-1.85804 1
< 0.1%
ValueCountFrequency (%)
0 2249
0.3%
-1 × 10-516
 
< 0.1%
-2 × 10-522
 
< 0.1%
-3 × 10-516
 
< 0.1%
-4 × 10-514
 
< 0.1%
-5 × 10-517
 
< 0.1%
-6 × 10-59
 
< 0.1%
-7 × 10-511
 
< 0.1%
-8 × 10-517
 
< 0.1%
-9 × 10-525
 
< 0.1%

C_RHO
Real number (ℝ)

Distinct157245
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80027616
Minimum-858.74562
Maximum474.04912
Zeros2252
Zeros (%)0.3%
Negative7061
Negative (%)1.0%
Memory size5.7 MiB
2023-10-07T23:52:35.911420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-858.74562
5-th percentile0.00167
Q10.04267
median0.16253
Q30.54346
95-th percentile1.4547475
Maximum474.04912
Range1332.7947
Interquartile range (IQR)0.50079

Descriptive statistics

Standard deviation8.1475559
Coefficient of variation (CV)10.18093
Kurtosis2232.6923
Mean0.80027616
Median Absolute Deviation (MAD)0.14969
Skewness-3.6587131
Sum592823.77
Variance66.382667
MonotonicityNot monotonic
2023-10-07T23:52:36.220298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2252
 
0.3%
0.00049 323
 
< 0.1%
0.0003 288
 
< 0.1%
0.0005 287
 
< 0.1%
0.00048 284
 
< 0.1%
0.00051 278
 
< 0.1%
0.00039 276
 
< 0.1%
0.00046 272
 
< 0.1%
0.00037 269
 
< 0.1%
0.00023 268
 
< 0.1%
Other values (157235) 735977
99.4%
ValueCountFrequency (%)
-858.74562 1
< 0.1%
-822.00009 1
< 0.1%
-809.50299 1
< 0.1%
-797.50458 1
< 0.1%
-785.50469 1
< 0.1%
-773.00351 1
< 0.1%
-748.2511 1
< 0.1%
-724.25042 1
< 0.1%
-700.5009 1
< 0.1%
-682.50012 1
< 0.1%
ValueCountFrequency (%)
474.04912 1
< 0.1%
451.152 1
< 0.1%
437.59146 1
< 0.1%
434.28842 1
< 0.1%
426.81106 1
< 0.1%
419.92333 1
< 0.1%
416.98929 1
< 0.1%
415.95451 1
< 0.1%
411.73553 1
< 0.1%
407.28774 1
< 0.1%

C_IV
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct109104
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46240187
Minimum-0.0005
Maximum61.72025
Zeros53
Zeros (%)< 0.1%
Negative2393
Negative (%)0.3%
Memory size5.7 MiB
2023-10-07T23:52:36.517532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.0005
5-th percentile0.21819
Q10.28282
median0.33931
Q30.44694
95-th percentile1.0398335
Maximum61.72025
Range61.72075
Interquartile range (IQR)0.16412

Descriptive statistics

Standard deviation0.68060993
Coefficient of variation (CV)1.4719013
Kurtosis1242.1237
Mean0.46240187
Median Absolute Deviation (MAD)0.070665
Skewness25.821662
Sum342535.28
Variance0.46322988
MonotonicityNot monotonic
2023-10-07T23:52:36.804405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3 × 10-574
 
< 0.1%
0.00034 67
 
< 0.1%
0.00042 66
 
< 0.1%
0.00027 62
 
< 0.1%
0.29552 61
 
< 0.1%
-5 × 10-561
 
< 0.1%
0.00021 61
 
< 0.1%
0.00026 60
 
< 0.1%
0.29403 60
 
< 0.1%
0.29294 59
 
< 0.1%
Other values (109094) 740143
99.9%
ValueCountFrequency (%)
-0.0005 21
 
< 0.1%
-0.00049 49
< 0.1%
-0.00048 43
< 0.1%
-0.00047 49
< 0.1%
-0.00046 51
< 0.1%
-0.00045 58
< 0.1%
-0.00044 53
< 0.1%
-0.00043 45
< 0.1%
-0.00042 46
< 0.1%
-0.00041 31
< 0.1%
ValueCountFrequency (%)
61.72025 1
< 0.1%
55.83797 1
< 0.1%
54.42318 1
< 0.1%
54.01149 1
< 0.1%
51.29721 1
< 0.1%
50.72417 1
< 0.1%
50.49236 1
< 0.1%
50.18894 1
< 0.1%
48.55927 1
< 0.1%
47.79657 1
< 0.1%

C_VOLUME
Real number (ℝ)

Distinct7200
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.65131
Minimum1
Maximum183723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-10-07T23:52:37.118907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median22
Q3115
95-th percentile645
Maximum183723
Range183722
Interquartile range (IQR)111

Descriptive statistics

Standard deviation1569.2255
Coefficient of variation (CV)7.7054526
Kurtosis1887.4
Mean203.65131
Median Absolute Deviation (MAD)21
Skewness35.219036
Sum1.508596 × 108
Variance2462468.7
MonotonicityNot monotonic
2023-10-07T23:52:37.410621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 90691
 
12.2%
2 49477
 
6.7%
3 28385
 
3.8%
4 24119
 
3.3%
5 23643
 
3.2%
10 18933
 
2.6%
6 15750
 
2.1%
7 12520
 
1.7%
8 11982
 
1.6%
11 10750
 
1.5%
Other values (7190) 454524
61.4%
ValueCountFrequency (%)
1 90691
12.2%
2 49477
6.7%
3 28385
 
3.8%
4 24119
 
3.3%
5 23643
 
3.2%
6 15750
 
2.1%
7 12520
 
1.7%
8 11982
 
1.6%
9 9086
 
1.2%
10 18933
 
2.6%
ValueCountFrequency (%)
183723 1
< 0.1%
138469 1
< 0.1%
135213 1
< 0.1%
131280 1
< 0.1%
130860 1
< 0.1%
128935 1
< 0.1%
122894 1
< 0.1%
122412 1
< 0.1%
120733 1
< 0.1%
115426 1
< 0.1%

C_LAST
Real number (ℝ)

Distinct17437
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.816217
Minimum0.01
Maximum396.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-10-07T23:52:37.700371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.1
Q14.6
median18.05
Q347.4
95-th percentile117.3
Maximum396.1
Range396.09
Interquartile range (IQR)42.8

Descriptive statistics

Standard deviation42.71999
Coefficient of variation (CV)1.2632989
Kurtosis7.5928274
Mean33.816217
Median Absolute Deviation (MAD)16.21
Skewness2.3479908
Sum25050174
Variance1824.9975
MonotonicityNot monotonic
2023-10-07T23:52:38.000378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 13071
 
1.8%
0.02 4994
 
0.7%
0.03 3777
 
0.5%
0.04 2920
 
0.4%
0.05 2746
 
0.4%
0.06 2332
 
0.3%
0.07 2068
 
0.3%
0.08 1904
 
0.3%
0.1 1666
 
0.2%
0.09 1614
 
0.2%
Other values (17427) 703682
95.0%
ValueCountFrequency (%)
0.01 13071
1.8%
0.02 4994
 
0.7%
0.03 3777
 
0.5%
0.04 2920
 
0.4%
0.05 2746
 
0.4%
0.06 2332
 
0.3%
0.07 2068
 
0.3%
0.08 1904
 
0.3%
0.09 1614
 
0.2%
0.1 1666
 
0.2%
ValueCountFrequency (%)
396.1 2
 
< 0.1%
392.95 1
 
< 0.1%
389.6 1
 
< 0.1%
387.53 1
 
< 0.1%
386.4 4
 
< 0.1%
386.3 1
 
< 0.1%
377.81 10
< 0.1%
377.6 8
< 0.1%
376 1
 
< 0.1%
375.85 3
 
< 0.1%

C_SIZE
Categorical

Distinct193090
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
1 x 1
 
8067
10 x 10
 
5062
20 x 20
 
3121
60 x 60
 
2431
100 x 100
 
2282
Other values (193085)
719811 

Length

Max length14
Median length13
Mean length8.5405819
Min length6

Characters and Unicode

Total characters6326641
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122182 ?
Unique (%)16.5%

Sample

1st row 95 x 179
2nd row 1741 x 532
3rd row 64 x 10
4th row 335 x 316
5th row 689 x 484

Common Values

ValueCountFrequency (%)
1 x 1 8067
 
1.1%
10 x 10 5062
 
0.7%
20 x 20 3121
 
0.4%
60 x 60 2431
 
0.3%
100 x 100 2282
 
0.3%
90 x 90 2135
 
0.3%
2 x 1 1923
 
0.3%
1 x 2 1776
 
0.2%
2 x 2 1750
 
0.2%
21 x 21 1703
 
0.2%
Other values (193080) 710524
95.9%

Length

2023-10-07T23:52:38.324677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
x 740774
33.3%
1 69284
 
3.1%
10 42398
 
1.9%
20 41389
 
1.9%
2 28541
 
1.3%
21 23631
 
1.1%
5 22093
 
1.0%
30 19252
 
0.9%
60 18680
 
0.8%
11 18550
 
0.8%
Other values (2815) 1197730
53.9%

Most occurring characters

ValueCountFrequency (%)
2222322
35.1%
x 740774
 
11.7%
1 736901
 
11.6%
2 454319
 
7.2%
0 368355
 
5.8%
3 341405
 
5.4%
5 295283
 
4.7%
4 277200
 
4.4%
6 258978
 
4.1%
7 223997
 
3.5%
Other values (2) 407107
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3363545
53.2%
Space Separator 2222322
35.1%
Lowercase Letter 740774
 
11.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 736901
21.9%
2 454319
13.5%
0 368355
11.0%
3 341405
10.2%
5 295283
8.8%
4 277200
 
8.2%
6 258978
 
7.7%
7 223997
 
6.7%
9 204500
 
6.1%
8 202607
 
6.0%
Space Separator
ValueCountFrequency (%)
2222322
100.0%
Lowercase Letter
ValueCountFrequency (%)
x 740774
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5585867
88.3%
Latin 740774
 
11.7%

Most frequent character per script

Common
ValueCountFrequency (%)
2222322
39.8%
1 736901
 
13.2%
2 454319
 
8.1%
0 368355
 
6.6%
3 341405
 
6.1%
5 295283
 
5.3%
4 277200
 
5.0%
6 258978
 
4.6%
7 223997
 
4.0%
9 204500
 
3.7%
Latin
ValueCountFrequency (%)
x 740774
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6326641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2222322
35.1%
x 740774
 
11.7%
1 736901
 
11.6%
2 454319
 
7.2%
0 368355
 
5.8%
3 341405
 
5.4%
5 295283
 
4.7%
4 277200
 
4.4%
6 258978
 
4.1%
7 223997
 
3.5%
Other values (2) 407107
 
6.4%

C_BID
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16551
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.236765
Minimum0
Maximum429.06
Zeros14468
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-10-07T23:52:38.599355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.09
Q14.5
median17.85
Q347.05
95-th percentile120.25
Maximum429.06
Range429.06
Interquartile range (IQR)42.55

Descriptive statistics

Standard deviation44.792394
Coefficient of variation (CV)1.3083127
Kurtosis9.0381223
Mean34.236765
Median Absolute Deviation (MAD)16.09
Skewness2.5523002
Sum25361706
Variance2006.3585
MonotonicityNot monotonic
2023-10-07T23:52:38.872380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14468
 
2.0%
0.01 5033
 
0.7%
0.02 3739
 
0.5%
0.03 2986
 
0.4%
0.05 2467
 
0.3%
0.04 2369
 
0.3%
0.06 2017
 
0.3%
0.07 1877
 
0.3%
0.08 1645
 
0.2%
0.1 1624
 
0.2%
Other values (16541) 702549
94.8%
ValueCountFrequency (%)
0 14468
2.0%
0.01 5033
 
0.7%
0.02 3739
 
0.5%
0.03 2986
 
0.4%
0.04 2369
 
0.3%
0.05 2467
 
0.3%
0.06 2017
 
0.3%
0.07 1877
 
0.3%
0.08 1645
 
0.2%
0.09 1480
 
0.2%
ValueCountFrequency (%)
429.06 1
< 0.1%
423.8 1
< 0.1%
423.7 1
< 0.1%
422.95 1
< 0.1%
422.81 1
< 0.1%
422.71 1
< 0.1%
422.2 1
< 0.1%
421.9 1
< 0.1%
420.91 1
< 0.1%
418.9 1
< 0.1%

C_ASK
Real number (ℝ)

Distinct16687
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.95558
Minimum0
Maximum433.39
Zeros1209
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-10-07T23:52:39.187491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q14.75
median18.35
Q348.11
95-th percentile122.5
Maximum433.39
Range433.39
Interquartile range (IQR)43.36

Descriptive statistics

Standard deviation45.439213
Coefficient of variation (CV)1.299913
Kurtosis8.9065231
Mean34.95558
Median Absolute Deviation (MAD)16.45
Skewness2.5357851
Sum25894184
Variance2064.7221
MonotonicityNot monotonic
2023-10-07T23:52:39.474137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 8686
 
1.2%
0.02 5547
 
0.7%
0.03 3949
 
0.5%
0.04 3210
 
0.4%
0.05 2727
 
0.4%
0.06 2234
 
0.3%
0.07 2130
 
0.3%
0.08 1922
 
0.3%
0.09 1770
 
0.2%
0.1 1732
 
0.2%
Other values (16677) 706867
95.4%
ValueCountFrequency (%)
0 1209
 
0.2%
0.01 8686
1.2%
0.02 5547
0.7%
0.03 3949
0.5%
0.04 3210
 
0.4%
0.05 2727
 
0.4%
0.06 2234
 
0.3%
0.07 2130
 
0.3%
0.08 1922
 
0.3%
0.09 1770
 
0.2%
ValueCountFrequency (%)
433.39 1
< 0.1%
427.56 1
< 0.1%
427.25 1
< 0.1%
427.01 1
< 0.1%
427 1
< 0.1%
426.5 1
< 0.1%
426.05 1
< 0.1%
425.41 1
< 0.1%
425.25 1
< 0.1%
422.61 1
< 0.1%

STRIKE
Real number (ℝ)

Distinct447
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.37161
Minimum2.5
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-10-07T23:52:39.752882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile65
Q1115
median155
Q3200
95-th percentile335
Maximum1000
Range997.5
Interquartile range (IQR)85

Descriptive statistics

Standard deviation82.578649
Coefficient of variation (CV)0.48755897
Kurtosis3.1586629
Mean169.37161
Median Absolute Deviation (MAD)42.5
Skewness1.4223541
Sum1.2546609 × 108
Variance6819.2333
MonotonicityNot monotonic
2023-10-07T23:52:40.053281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 18703
 
2.5%
150 18534
 
2.5%
170 18395
 
2.5%
165 18124
 
2.4%
155 18087
 
2.4%
175 17829
 
2.4%
180 17487
 
2.4%
145 17404
 
2.3%
140 17382
 
2.3%
185 16181
 
2.2%
Other values (437) 562648
76.0%
ValueCountFrequency (%)
2.5 46
 
< 0.1%
5 2
 
< 0.1%
7.5 17
 
< 0.1%
10 2
 
< 0.1%
15 1
 
< 0.1%
17.5 19
 
< 0.1%
18.75 133
< 0.1%
20 176
< 0.1%
21.25 101
< 0.1%
22.5 220
< 0.1%
ValueCountFrequency (%)
1000 5
 
< 0.1%
900 6
 
< 0.1%
800 1
 
< 0.1%
780 1
 
< 0.1%
750 2
 
< 0.1%
740 3
 
< 0.1%
730 1
 
< 0.1%
725 5
 
< 0.1%
720 18
< 0.1%
710 2
 
< 0.1%

STRIKE_DISTANCE
Real number (ℝ)

Distinct3118
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.107127
Minimum0
Maximum500.8
Zeros1113
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-10-07T23:52:40.345478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4
Q112.1
median29.6
Q361.1
95-th percentile133.4
Maximum500.8
Range500.8
Interquartile range (IQR)49

Descriptive statistics

Standard deviation45.828131
Coefficient of variation (CV)1.0390187
Kurtosis7.0167252
Mean44.107127
Median Absolute Deviation (MAD)20.9
Skewness2.1923291
Sum32673413
Variance2100.2176
MonotonicityNot monotonic
2023-10-07T23:52:40.634377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 2415
 
0.3%
10 2149
 
0.3%
4 2001
 
0.3%
2 1975
 
0.3%
3 1968
 
0.3%
7 1952
 
0.3%
6 1938
 
0.3%
8 1895
 
0.3%
1 1864
 
0.3%
15 1856
 
0.3%
Other values (3108) 720761
97.3%
ValueCountFrequency (%)
0 1113
0.2%
0.1 1623
0.2%
0.2 1488
0.2%
0.3 1491
0.2%
0.4 1370
0.2%
0.5 1560
0.2%
0.6 1295
0.2%
0.7 1354
0.2%
0.8 1663
0.2%
0.9 1578
0.2%
ValueCountFrequency (%)
500.8 2
< 0.1%
500 3
< 0.1%
447.8 1
 
< 0.1%
440.2 1
 
< 0.1%
440 1
 
< 0.1%
431.2 1
 
< 0.1%
425 2
< 0.1%
424.3 2
< 0.1%
424.2 2
< 0.1%
422.5 2
< 0.1%

STRIKE_DISTANCE_PCT
Real number (ℝ)

Distinct1423
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23673155
Minimum0
Maximum1.895
Zeros1567
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-10-07T23:52:40.919429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.015
Q10.075
median0.174
Q30.35
95-th percentile0.652
Maximum1.895
Range1.895
Interquartile range (IQR)0.275

Descriptive statistics

Standard deviation0.2061734
Coefficient of variation (CV)0.87091647
Kurtosis1.4725355
Mean0.23673155
Median Absolute Deviation (MAD)0.118
Skewness1.2263407
Sum175364.57
Variance0.042507472
MonotonicityNot monotonic
2023-10-07T23:52:41.214973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.017 2805
 
0.4%
0.046 2770
 
0.4%
0.031 2751
 
0.4%
0.029 2736
 
0.4%
0.025 2713
 
0.4%
0.04 2690
 
0.4%
0.027 2669
 
0.4%
0.059 2668
 
0.4%
0.03 2656
 
0.4%
0.043 2641
 
0.4%
Other values (1413) 713675
96.3%
ValueCountFrequency (%)
0 1567
0.2%
0.001 2326
0.3%
0.002 2406
0.3%
0.003 2279
0.3%
0.004 2223
0.3%
0.005 2425
0.3%
0.006 2400
0.3%
0.007 2237
0.3%
0.008 2554
0.3%
0.009 2453
0.3%
ValueCountFrequency (%)
1.895 1
< 0.1%
1.874 1
< 0.1%
1.86 1
< 0.1%
1.84 1
< 0.1%
1.833 2
< 0.1%
1.8 1
< 0.1%
1.784 1
< 0.1%
1.777 1
< 0.1%
1.765 1
< 0.1%
1.763 1
< 0.1%

Interactions

2023-10-07T23:52:17.054521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:51.093364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:56.204964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:02.163555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:07.856680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:12.934151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:19.643264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:24.491519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:29.760399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:36.044573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:40.972414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:47.436034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:52.577877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:58.158084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:04.290940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:10.699252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:17.380453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:51.386402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:56.505065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:02.615516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:08.155612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:13.240088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:19.942722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:24.779371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:30.231314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:36.338234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:41.277171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:47.749670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:52.866139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:58.609554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:04.602254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:10.995573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:17.711690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:51.706318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:56.832465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:03.107029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:08.495802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:13.570426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:20.265698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:25.103379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:30.701349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:36.651715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:41.603186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:48.086310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:53.190507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:59.092567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:04.918873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:11.320364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:18.054709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:52.009488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:57.144209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:03.572928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:08.809240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:14.014856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:20.560078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:25.411638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:31.147046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:36.949512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:41.916570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:48.396241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:53.490567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:59.502279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:05.298872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:11.625884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:18.378731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:52.318989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:57.455592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:03.893715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:09.125441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:14.428958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:20.874448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:25.703939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:31.566610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:37.260886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:42.228613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:48.701304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:53.779560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:59.958297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:05.798458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:11.940883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:18.697858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:52.617429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:57.768262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:04.210437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:09.447627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:14.900060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:21.158141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:26.016989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:31.994009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:37.570307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:42.535269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:49.035792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:54.086384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:00.420178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:06.266894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:12.375496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:19.024702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:52.924087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:58.080194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:04.499398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:09.753016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:15.338472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:21.445230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:26.305627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:32.467562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:37.859880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:42.839304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:49.347914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:54.386158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:00.856890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:06.705225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:12.814327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:19.357475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:53.224205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:58.394186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:04.804940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:10.065068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:15.800331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:21.736653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:26.600093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:32.875588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:38.166303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:43.264995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:49.673815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:54.670990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:01.331682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:07.182925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:13.167718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:19.689807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:53.537247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:58.703210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:05.107792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:10.394656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:16.259007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:22.052348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:26.909004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:33.194153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:38.492847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:43.734883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:50.010494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:55.464058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:01.757020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:07.623626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:13.643556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:20.024942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:53.861696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:59.050848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:05.665338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:10.705005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:16.710540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:22.364106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:27.233724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:33.510404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:38.802481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:44.203984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:50.332848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:55.768454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:02.129045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:08.058101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:14.146416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:20.368411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:54.373306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:59.434468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:05.980597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:11.028429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:17.151891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:22.674804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:27.545128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:33.825990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:39.116883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:44.696870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:50.656991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:56.101984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:02.456383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:08.516422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:14.582371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:20.698040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:54.698973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:59.873491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:06.313200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:11.358957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:17.664648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:23.012538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:27.857346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:34.140923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:39.443974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:45.154777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:50.984033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:56.420331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:02.775505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:08.954829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:15.031777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:21.014074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:55.002836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:00.331574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:06.603830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:11.669029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:18.054067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:23.302978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:28.167581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:34.450446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:39.741388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:45.566887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:51.297570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:56.704992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:03.065408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:09.378255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:15.446452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:21.335364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:55.296653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:00.780162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:06.899407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:11.972662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:18.408228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:23.583931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:28.562258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:34.756269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:40.052312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:46.044095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:51.606872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:57.004244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:03.365152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:09.776270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:15.902561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:21.656420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:55.588391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:01.260502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:07.220627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:12.289321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:18.714863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:23.889004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:29.026288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:35.051352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:40.353663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:46.440989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:51.935450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:57.322257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:03.663495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:10.082559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:16.347324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:21.969747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:50:55.897805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:01.660302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:07.536451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:12.606605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:19.004847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:24.184345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:29.390510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:35.377361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:40.656422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:46.922934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:52.253793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:51:57.706114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:03.969601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:10.381151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-07T23:52:16.712633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-07T23:52:41.482475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
UNDERLYING_LASTEXPIRE_UNIXDTEC_DELTAC_GAMMAC_VEGAC_THETAC_RHOC_IVC_VOLUMEC_LASTC_BIDC_ASKSTRIKESTRIKE_DISTANCESTRIKE_DISTANCE_PCT
UNDERLYING_LAST1.000-0.0840.0380.050-0.2870.304-0.3540.1770.023-0.1000.2910.2910.2960.6650.3020.016
EXPIRE_UNIX-0.0841.0000.344-0.019-0.1340.2000.1290.3060.102-0.1080.0950.0930.095-0.0370.1250.174
DTE0.0380.3441.000-0.051-0.2410.7650.4520.833-0.152-0.2030.3330.3280.334-0.0030.2710.288
C_DELTA0.050-0.019-0.0511.000-0.349-0.205-0.0980.2200.498-0.4180.8360.8390.835-0.5560.2240.217
C_GAMMA-0.287-0.134-0.241-0.3491.0000.155-0.412-0.124-0.6030.471-0.561-0.560-0.5640.056-0.867-0.823
C_VEGA0.3040.2000.765-0.2050.1551.000-0.0760.793-0.3420.0150.1840.1800.1860.332-0.114-0.214
C_THETA-0.3540.1290.452-0.098-0.412-0.0761.0000.154-0.098-0.173-0.051-0.053-0.052-0.2420.3690.525
C_RHO0.1770.3060.8330.220-0.1240.7930.1541.000-0.091-0.2200.5350.5320.536-0.0250.0860.038
C_IV0.0230.102-0.1520.498-0.603-0.342-0.098-0.0911.000-0.3520.5340.5350.534-0.3060.4930.497
C_VOLUME-0.100-0.108-0.203-0.4180.4710.015-0.173-0.220-0.3521.000-0.477-0.476-0.4800.179-0.439-0.430
C_LAST0.2910.0950.3330.836-0.5610.184-0.0510.5350.534-0.4771.0000.9990.999-0.3640.4280.354
C_BID0.2910.0930.3280.839-0.5600.180-0.0530.5320.535-0.4760.9991.0001.000-0.3660.4270.353
C_ASK0.2960.0950.3340.835-0.5640.186-0.0520.5360.534-0.4800.9991.0001.000-0.3590.4310.356
STRIKE0.665-0.037-0.003-0.5560.0560.332-0.242-0.025-0.3060.179-0.364-0.366-0.3591.0000.061-0.158
STRIKE_DISTANCE0.3020.1250.2710.224-0.867-0.1140.3690.0860.493-0.4390.4280.4270.4310.0611.0000.946
STRIKE_DISTANCE_PCT0.0160.1740.2880.217-0.823-0.2140.5250.0380.497-0.4300.3540.3530.356-0.1580.9461.000

Missing values

2023-10-07T23:52:23.086691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-07T23:52:24.585607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

QUOTE_DATEUNDERLYING_LASTEXPIRE_DATEEXPIRE_UNIXDTEC_DELTAC_GAMMAC_VEGAC_THETAC_RHOC_IVC_VOLUMEC_LASTC_SIZEC_BIDC_ASKSTRIKESTRIKE_DISTANCESTRIKE_DISTANCE_PCT
02016-01-04105.352016-07-151468612800192.960.261810.016440.24937-0.015430.128030.25910688.02.5995 x 1792.852.92120.014.70.139
12016-01-04105.352016-04-151460750400101.960.706690.020610.19101-0.026130.154540.301834.010.551741 x 53210.8011.0197.57.80.075
22016-01-04105.352016-04-151460750400101.960.651540.022560.20611-0.027470.149800.29516688.09.1064 x 109.209.25100.05.30.051
32016-01-04105.352016-04-151460750400101.960.280020.022830.18816-0.022740.075690.26419655.02.38335 x 3162.352.43115.09.70.092
42016-01-04105.352016-04-151460750400101.960.067290.009010.07298-0.009070.019320.25771260.00.42689 x 4840.380.43130.024.70.234
52016-01-04105.352016-04-151460750400101.960.041820.006030.05037-0.006570.012050.26220199.00.25175 x 6260.210.27135.029.70.281
62016-01-04105.352016-04-151460750400101.960.025060.003960.03337-0.004010.007000.26767106.00.15447 x 40.120.15140.034.70.329
72016-01-04105.352016-04-151460750400101.960.017030.002710.02352-0.003390.004680.2774130.00.101032 x 7320.060.12145.039.70.376
82016-01-04105.352016-04-151460750400101.960.011050.001750.01551-0.001700.002600.28340164.00.081403 x 7370.020.09150.044.70.424
92016-01-04105.352016-06-171466193600164.960.911030.001580.08025-0.010980.052640.5112710.039.2048 x 4140.3040.8065.040.30.383
QUOTE_DATEUNDERLYING_LASTEXPIRE_DATEEXPIRE_UNIXDTEC_DELTAC_GAMMAC_VEGAC_THETAC_RHOC_IVC_VOLUMEC_LASTC_SIZEC_BIDC_ASKSTRIKESTRIKE_DISTANCESTRIKE_DISTANCE_PCT
7407642023-06-30193.782023-08-18169238880049.000.785870.014650.20599-0.063250.185140.27468711.016.824 x 816.6016.70180.013.80.071
7407652023-06-30193.782023-08-18169238880049.000.713530.018680.24139-0.066810.171070.253571653.012.5048 x 11812.5012.65185.08.80.045
7407662023-06-30193.782023-08-18169238880049.000.616630.022770.27109-0.068680.150110.233115276.08.95519 x 138.858.95190.03.80.020
7407672023-06-30193.782023-08-18169238880049.000.495880.025420.28387-0.066990.123230.218223018.05.8720 x 325.855.90195.01.20.006
7407682023-06-30193.782023-08-18169238880049.000.364210.025430.26764-0.058590.090910.205434634.03.525 x 4143.503.55200.06.20.032
7407692023-06-30193.782023-08-18169238880049.000.239880.022060.22215-0.046080.060250.196483189.01.94130 x 891.891.94205.011.20.058
7407702023-06-30193.782023-08-18169238880049.000.081060.010890.10746-0.020920.020280.191271378.00.50103 x 100.480.50215.021.20.110
7407712023-06-30193.782023-09-15169480800077.000.973970.000520.04964-0.038010.111921.208582.0118.8090 x 65118.55121.3575.0118.80.613
7407722023-06-30193.782023-08-18169238880049.000.143020.016500.16193-0.032280.036590.191452771.00.98191 x 950.960.99210.016.20.084
7407732023-06-30193.782025-12-191766178000903.040.405070.005331.15443-0.020541.525420.231677.017.1058 x 21715.5018.60245.051.20.264